Language is processed on a more or less word-by-word basis, and the processing difficulty
induced by each word is affected by our prior linguistic experience as well as our general knowledge
about the world. Surprisal and entropy reduction have been independently proposed as linking
theories between word processing difficulty and probabilistic language models. Extant models, however,
are typically limited to capturing linguistic experience and hence cannot account for the influence of
world knowledge. A recent comprehension model by Venhuizen, Crocker, and Brouwer (2019, Discourse
Processes) improves upon this situation by instantiating a comprehension-centric metric of surprisal that
integrates linguistic experience and world knowledge at the level of interpretation and combines them in
determining online expectations. Here, we extend this work by deriving a comprehension-centric metric
of entropy reduction from this model. In contrast to previous work, which has found that surprisal and
entropy reduction are not easily dissociated, we do find a clear dissociation in our model. While both
surprisal and entropy reduction derive from the same cognitive process—the word-by-word updating
of the unfolding interpretation—they reflect different aspects of this process: state-by-state expectation
(surprisal) versus end-state confirmation (entropy reduction)